When used disingenuously, explainable ML methods can provide cover for misused or intentionally abusive black-boxes [1]}, [2]}, [3]}. Explainable ML methods can also enable hacking or stealing of models or data through public prediction APIs or other endpoints [4]}, [5]}. Moreover, explainable ML methods are likely to be used for other nefarious purposes in the future and may be used for unknown destructive purposes now. Responsible practitioners need to understand the malevolent side of this technology to better detect and correct misuse and abuse.
